earthkit-meteo
A Python library for meteorological computations
Decision gist · record as of 2026-08-14
Yes, if you work with atmospheric or weather data and need standard meteorological calculations. The package is actively maintained, has low install friction, carries a permissive license, and supports modern Python versions. It is worth installing for meteorological workflows, especially if you already use NumPy or Torch and want to avoid reimplementing atmospheric physics.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low install friction; pure Python wheel with three lightweight runtime dependencies (deprecation, earthkit-utils, numpy).
- Actively maintained with a recent release 9 days ago.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must retain license notices and copyright attribution.
last release 2026-08-05 (9 days) · last repo commit 2026-08-11 · 16 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 76,528 downloads/mo, #14,618 on PyPI
Alternatives
Verify before relying
pip install earthkit-meteo
from earthkit.meteo import thermo
import numpy as np
t = np.array([264.12, 261.45]) # Kelvins
p = np.array([850, 850]) * 100.0 # Pascals
theta = thermo.potential_temperature(t, p)- Whether Torch and CuPy support is optional or requires separate installation.
- Full scope of meteorological functions beyond potential temperature.
- Performance characteristics when working with large datasets or GPU tensors.
What it is and what it does
earthkit-meteo is a meteorological computation library from ECMWF that wraps standard atmospheric physics calculations to work with multiple array backends. It lets you compute thermodynamic properties like potential temperature from temperature and pressure data, accepting NumPy arrays, Torch tensors, CuPy arrays, xarray objects, or fieldlist formats as input. The library is part of the broader earthkit ecosystem and is classified as Graduated and Production/Stable.
You use it when you need to perform standard meteorological calculations on weather or atmospheric data without writing the physics formulas yourself. It abstracts away the array-backend differences so the same code works whether your data lives in NumPy, on a GPU via Torch, or in other formats. The package has low install friction, depends only on deprecation, earthkit-utils, and numpy at runtime, and is actively maintained.
Use it for
- Compute potential temperature from model or observational data for atmospheric analysis.
- Process weather datasets in multiple array formats without rewriting calculation logic.
- Integrate meteorological computations into data pipelines using NumPy or Torch tensors.
- Perform thermodynamic calculations as part of ECMWF earthkit-based workflows.
- Accelerate atmospheric physics on GPUs by passing CuPy or Torch arrays to the library.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with atmospheric or weather data and need standard meteorological calculations.
The package is actively maintained, has low install friction, carries a permissive license, and supports modern Python versions. It is worth installing for meteorological workflows, especially if you already use NumPy or Torch and want to avoid reimplementing atmospheric physics.
Install
earthkit-meteo on PyPI
Before you install
Low install friction; pure Python wheel with three lightweight runtime dependencies (deprecation, earthkit-utils, numpy). Actively maintained with a recent release 9 days ago.
Requires Python 3.10 or later.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must retain license notices and copyright attribution.
Quickstart
pip install earthkit-meteo
from earthkit.meteo import thermo
import numpy as np
t = np.array([264.12, 261.45]) # Kelvins
p = np.array([850, 850]) * 100.0 # Pascals
theta = thermo.potential_temperature(t, p)Verify before relying
- Whether Torch and CuPy support is optional or requires separate installation.
- Full scope of meteorological functions beyond potential temperature.
- Performance characteristics when working with large datasets or GPU tensors.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesdeprecationearthkit-utilsnumpy |
| Maintenance | Actively maintained 9 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 76,528 / month, #14,618 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Scientific/Engineering |
Evidence: earthkit_meteo-1.1.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “meteorological calculations python”
- earthkit-meteoComputes meteorological quantities (potential temperature,…
- MetPyMetPy provides tools for reading, visualizing, and performing…
- eccodeslibeccodeslib provides Python bindings to decode and encode…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also earthkit-utils · MetPy · earthkit-data · AI-WQ-package · metar · meteostat · atcf-data-parser · herbie-data · eccodeslib · ecmwf-datastores-client